CN104965997B - A kind of virtual breeding method of crop based on plant function and structural model - Google Patents
A kind of virtual breeding method of crop based on plant function and structural model Download PDFInfo
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Abstract
A kind of virtual breeding method of crop based on plant function and structural model.This method will combine physiology and structure crop virtual growth model and quantity hereditary information, by simulating the growth and development process of crop, and reproductive process, reach the purpose of virtual breeding, its step is：Gather physiological data, morphological data, quantitative gene data and the growing environment data structure raw data set of crop；Based on initial data, crop function and structural model are built by the method and physiological ecology of crop principle of computer graphics；Hereditary module is built, comprising quantitative inheritance information, establishes the association from quantitative inheritance information to correlation model parameters；The simulation to the genetic manipulation such as intersection, restructuring in crossover process is added, realizes the simulation to crop reproductive process；By to selection and hybridization individual in model, realizing virtual breeding；The virtual breeding for proposing that computer technology can be utilized to realize crop of the present invention, so as to provide auxiliary for traditional breeding method process and support.
Description
Technical field
Present invention is related to plant physioecology, plant visual modeling and crop breeding field, is a kind of energy
Enough methods that crop breeding process is simulated by way of three-dimensional visualization model, can operate with the auxiliary to field crops seed selection
And support.
Background technology
Traditional breeding method has more perfect a breeding theory and technical system, and the development of new technology, including biotechnology,
Gene technology etc., achievement and the development of crop breeding can be promoted.Assistant breeding mode emerging at present has molecular labeling auxiliary
Breeding, cell engineering breeding and genetic engineering breeding etc., by the technological means such as molecular breeding, gene breeding and conventional breeding mode
Combine, the raising of breeding level can be greatly promoted.However, other new technologies outside conventional breeding mode are educated at present
Also all there is great limitation in kind means, including this respect research needs a large amount of technological accumulation, and implements and be also required to spy
Fixed instrument and equipment supports that this has resulted in the increase of research cost, so as to be taken off in evolution with actual breeding process
From.Even the field test of longer cycle is also required to reference to the breeding experiment of new technology, especially in target plant type not
Lack of targeted in the case of it is determined that.Further, since safety evaluatio and authentication mechanism are not perfect enough, related new breeding mode obtains
For the security of the cereal crops kind arrived also by very big query, this just allows the development of these new technologies and popularization to be limited
System.
Crop function and structural model are a kind of new visualization plant model constructing technologies of comparison, and it passes through in computer
On show the morphological development process of crop in a manner of 3-D graphic, and combine the physiology course of crop and specific environmental factor
To the regulating and controlling effect of morphological development, the change in time gradient to the structure in the space of crop can be realized.Recently, plant mould
Type researchers start the quantitative inheritance information of crop being also added in plant model, so as to build from crop quantitative gene,
Environmental factor is to major physiological process, then the so complete regulated and control network of developmental stage to morphosis.
Main plant function of the present invention is with based on structure modelling method, building hereditary module, realizing and crop was bred
The simulation of genetic manipulation in journey, and association of the hereditary information to objective trait is built, select main genetic correlation parameter pair
Growth trends carry out regulation and control influence, and the plasticity of phenotype is showed by the otherness of gene, final to simulate crop breeding process,
The virtual breeding of crop is realized, the new breeding techniques such as present molecular breeding can be combined, breeding practice is aided in.It is this
The it is proposed of virtual breeding method still belongs to the first time, and has preferably innovative and application value.
The content of the invention
It is an object of the invention to provide a kind of technology combined amount hereditary information mould based on plant function and structural modeling
The method of work done in the manner of a certain author thing breeding process, this method more can truly reflect the objective trait dynamic development of different genotype individual
Performance, so as to carry out virtual seed selection to the crop individual of simulation by way of computer visualization, and quickly obtain different marks
Accurate seed selection result, the final field breeding process for reality provide auxiliary and supported.
The technology of plant function and structural modeling, it is the means by computer visualization, considers plant growth
External cause --- climatic factor, edphic factor, manual operation factor etc., and related internal cause --- the major physiological of plant is entered
Journey, morphological development etc., the technology of simulation is realized to the Growth trends of plant.By adding number in plant function and structural model
Measure hereditary information, it becomes possible to build " gene and environment-physiology-form " interaction feedback network, and enter in a manner of Three-Dimensional Dynamic
Row shows；Along with the simulation to crop individual reproduction process and its genetic manipulation, the simulation to breeding process can be realized.
Technical scheme is used by the present invention realizes virtual breeding：
A kind of virtual breeding method of crop based on function and structural model is used and includes physiological function, morphological development, number
Measure the method simulation crop of hereditary information and the crop virtual growth model of specific environmental agents and manual selecting operation's combination
The breeding process of breeding, and comprise the steps of：
Step 1：Data acquisition
By way of the combination of field test, molecular test, assignment of genes gene mapping analysis and literature search, collection model structure institute
The data needed, establish raw data set, wherein including each side data of same target crop colony：Crop growth environment number
According to, form dynamic growth data, major physiological process data, the molecular labeling number related to genetic map data, objective trait
Measure gene data etc.；
Step 2：Function is built with structural model
First, with rule-based Plants modeling method, from computer graphics techniques, with plant growth principle
Based on, using extension XL (eXtended L-Systems) modeling language, Java programming languages and realize figure replace
RGG (Relational Growth Grammar) syntax rule, crop organ's form is rebuild, growth course is entered
Row simulation, establish Crop Structure model (by taking rice as an example, i.e., the visualization built up comprising organ morphologies such as stem, leaf, fringe, seeds
Model)；
Secondly, on the basis of structural model, the simulation of crop major physiological process is added：With LEAFC3 photosynthesis
Model, the photosynthetic parameters related by setting kind, the maximum carboxylation speed V under specified tempm25(μmol m-2s-1)、
Potential photosynthetic electron transfer speed J during light saturationm25(μmol m-2s-1), Photosynthetic Electron transmission activation evergy Ej(J/mol)、
CO2Kinetic parameter Kc25(mol/mol)、O2Kinetic parameter Ko25(mol/mol) etc., and the environment ginseng that kind is unrelated
Number, such as air themperature TaCO in (DEG C), air2Concentration Ca(mol/mol), the two-way length that relative humidity RH, blade absorb
Wave radiation intensity Ri(W/m2), the wind speed Wspeed (m/s) of horizontal direction etc., simulate photosynthetic related biochemical reaction in blade,
Stomatal conductance, the matter and energy transmission mechanism on blade border, Ecologial is published in Nikilov et al. in nineteen ninety-five
Upper (the volumes (phase) of Modelling：80(2-3)；The page number：Algorithm 205-235) calculates the CO of plant leaf blade2Short-term steady state flux,
Moisture and heat exchange, the speed of assimilation quotient is produced so as to simulate crop by leaf photosynthesis, is shown below：
An=f (Ri,Ta,...,Vm25,Jm25,...)
Here A is rememberednFor Net Photosynthetic Rate (μm ol m-2s-1), then the photosynthetic yield that each growth step-length is accumulated is：
Here, P is rememberedtFor photosynthetic yield (μm ol), aiFor the area (m of i-th blade2), n is crop plant individual blade
Number, Δ tdFor a growth step-length (s), i.e., the time span of one day in model, it is worth for 24 × 3600, in addition, it is assumed that institute
Some assimilation quotients are all pooled in an assimilation quotient pond being assigned to before organ, are designated as AP (μm ol), then t AP increment
As：
Δ AP=Pt-Gt
Here, G is rememberedtIt is for the amount of the assimilation quotient of plant respiration (including growth respiration and maintenance breathe) consumption, i.e., same
The instantaneous delta Δ AP in compound pond is time t photosynthetic yield PtWith the difference of consumption；GtCumulative by the increment of all organs
Come；The increment of certain organs is calculated by the organ growth function combination source storehouse model：Itd is proposed based on Yin etc. in 2003
Beta growth functions, can be calculated by following formula：
Remember cmFor maximum growth rate, tmFor growth rate maximum at the time of, te(i.e. growth speed at the time of to stop growing
When rate is 0), that is, the dimension of the organ reaches maximum dimension wmax(length m, area m2) at the time of (for example stem reaches
Maximum length or blade reach maximum area), and then the potential growth speed of t certain organs at any time can be calculated
grpot：
By taking the plant height of rice as an example, gr herepotThe as potential growth speed of t plant height, by the potential of all organs
Growth rate adds up, and is multiplied by time step Δ td, obtain the storehouse intensity sd of whole planttot：
sdtot=∑ sspotΔtd
Therefore, the ratio of the storehouse intensity size of individual plants can be accounted for according to the storehouse intensity of certain organs, this is calculated
Actual growth rate gr in organ growth step-lengthreal：
Here AP is the assimilation quotient pond size at current time；Pass through the calculating of these growth functions and partition function, energy
Enough realize that whole plant shows in the developmental stage of whole growth cycle；
Then, emphasis establishes illumination model in environmental model, the position of simulated solar light source and radiancy change；Virtual
Sunshine on high in be divided into direct light and scattering light, including they distribution in three dimensions, and reach crop
Blocking by canopy space after canopy, so as to realize the size of luminous flux in leaf photosynthesis；
Finally, language function-structural modeling technology, with reference to Crop Structure model, physiological models and environmental model, with the time
For axis, regularization explanation is done by formation of the syntax rule based on XL language and RGG to crop organ and growth, and realize
Rule and figure between iteration, replace, with reference to virtual Crop assimilation quotient formed and distribution, so as to combine physiological function,
Realize that virtual Crop Growth trends visualize on the basis of topological structure and luminous environment condition, acquisition can simulate plant growth
The function and structural model of journey；
Step 3：The structure of hereditary module
Increasing hereditary module for individual in model, the module includes the inherent quantitative inheritance attribute stored with array form,
It is designated as：
M={ m1,m2,…,mx,q1,mx+1,…,qi,…,mn}
D={ d12,d23,…,dxq1,…,d(n-1)n}
Here, M represents the molecular marker gene type in one of genome, and its sequence includes n molecular labeling, m1
To mn, the i quantitative trait locuses for studying to obtain by the assignment of genes gene mapping, q placed according to specific location information therebetween1To qi；
The each site value of sequence is 1 or -1, wherein 1 expression molecular labeling m or quantitative trait locus q comes from male parent, -1 table
Show that all molecular marker gene types are all 1 that is, in male parent from female parent, be all -1 in maternal；D represents corresponding molecular labeling
The genetic distance in (or between molecular labeling and quantity site), d between adjacent molecule marks in sequence(n-1)nRepresent the (n-1)th molecule
Genetic distance (Morgan) between mark and n-th of molecular labeling, separately there is an array IqStored number trait locuses institute
The positional information at place, i.e., the subscript in M：
Iq={ I1,I2,…,Ii}
Here, from I1To IiValue represent the 1st quantitative trait locus to the subscript of i-th of locus；
By the computing of above-mentioned two array, the genotype array of quantitative trait locus can be obtained：
Q={ x1,x2,…,xi}
Here array Q represents the genotype of quantitative gene in a genome, xiQ in as above-mentioned M arraysi, simultaneously
The effect value (being represented with array A) in quantitative gene site：
A={ a1,a2,…,ai}
aiRepresent the additive effect value of the quantitative trait locus on i-th of site；According further to actual conditions, aa is established
Epistatic analysis array (AA expressions) and epistatic gene type array (QaaRepresent), i.e., the additive effect and other one in one site
The reciprocal effects and its related locus genotype of the additive effect in individual site：
AA={ aa1,aa2,…,aaj}
Qaa={ xaa1,xaa2,…,xaaj}
aajRepresent j-th plus add epistasis effect value, xaajRepresent the genotype in j-th of epistatic analysis site；According to
These genotype informations and effect value information, along with the community average μ of objective trait, specific strain individual can be calculated
Objective trait Phenotypic value y：
y(Lk)=μ+G (Lk)
Here y (Lk) the individual objective trait Phenotypic value of k-th of strain is represented, by colony's mean μ and strain individual
Genetic effect value G (Lk) plus and and obtain；And genetic effect value is all sites additive effect aiWith loci gene type xi(Lk)Multiply
Long-pending is cumulative, along with all epistasis loci gene type xi(Lk)xj(Lk)With epistatic analysis value aaijProduct；
Here the objective trait Phenotypic value y (L being calculatedk) parameter as genetic correlation, it is updated in step 2
In growth function, instead of wmaxValue, i.e., the maximum growth dimension w in same character growth functionmaxFor genetic correlation, value by
Its genes of individuals offset sum amount site effect value determines；
By taking the plant height character of rice (diploid) as an example, with reference to the genotype data in two genomes and additivity and on
Position property effect Value Data, the Phenotypic value of plant height can be obtained；It is used in trunk diameter growth function and is used as maximum length wmax's
Value, so as to control assimilation quotient competitiveness and growth rate of the individual stem at each growth moment, and realize genotypic difference
To the association of phenotype difference；
Step 4：Simulate reproductive process
First, establish molecular labeling and intersect (crossing over) algorithm：
Step 1：If there is the molecular labeling not traveled through, using the molecular labeling not traveled through as entrance, calculate current adjacent
Exchange rate between molecular labeling (including adjacent molecular labeling and quantitative gene), is calculated by following formula：
Here r is exchange rate, and x is the distance between corresponding molecular labeling value in genetic distance array D in step 3；If
Whole molecular labelings all have stepped through, then perform Step 4；
Step 2：Using the exchange rate calculated as Probability Condition, exchange algorithm is performed, if into exchange algorithm sentence,
Perform Step 3；If being introduced into exchange algorithm sentence, Step 1 is performed；
Step 3：All molecular labelings after current molecular mark swap in two genomes；Perform
Step 1；
Step 4：Execution terminates, and obtains the target molecule marker genetype of corresponding two group chromosome groups；
Then, mainly with chromosome separation and restructuring (Recombination) algorithm simulation reproductive process：
Step 1：(by taking diploid as an example, two genomes for remembering male parent are M to the chromosome of Parent individual11And M12；
Maternal is designated as M21And M22), molecular labeling crossover algorithm is performed respectively, carries out Genome separation, the list after being exchanged
Times genome M’ 11、M’ 12、M’ 21、M’ 22；
Step 2：The two group chromosome group M that male parent isolates are taken with 50% probability’ 11、M’ 12In one group, then equally with
50% probability takes the maternal two group chromosome group M isolated’ 21、M’ 22In one group, be reassembled as filial generation individual chromosome
The composition of group；
Step 3：By the child chromosome group genotype of generation, objective trait is calculated with the method in step 3
The value of parameter, and the growth function applied；
Step 4：Required offspring individual number requirement is such as not up to, then re-executes Step 1；As reached filial generation
The requirement of body number, then this breeding terminate, and may be grown dynamic simulation or breeding next time；
Step 5：Carry out virtual selection and use
By taking seed selection DH colonies as an example, can by with crop function and structural model carry out virtual breeding be divided into it is following several
Step：
Step 1：Initialization model colony, including the related quantitative inheritance information of objective trait is set；
Step 2：Any stage of population growth visual Simulation in a model, select parent of two individuals as hybridization
This；
Step 3：To the genetic manipulation described in parent's execution step 4 of selection, reproductive process is simulated；
Step 4：Obtain son 1 generation colony：F1 generation, perform growth simulation；
Step 5：To continue seed selection, Step 2 is performed；If seed selection finishes, Step 6 is performed；
Step 6：Colony's Genome separation, monoploid is obtained, and carry out simulation and double；
Step 7：Obtain target group：DH colonies, perform growth simulation；
Step 8：The output such as the morphological data of target group, physiological data, genetic data can be obtained by model, it is empty
Intend Breeding Process to terminate；
In addition to the virtual seed selection of DH colonies, above-mentioned steps slightly change the seed selection that can realize other colonies, such as
RIL RIL colonies etc., overall process are that growth simulation → selection → breeding → growth simulation of filial generation → continues to select
→ continue breeding → new filial generation ... and so circulate, selected according to specific standard and target, with regard to that can be simulated
Progeny population, so as to realize the virtual breeding of general significance.
The beneficial effects of the invention are as follows：
1) method for combining Visualization Model, realizes the simulation to the virtual breeding process of specific crop first；
2) model includes environment, physiology, structure, the data of genetic correlation, builds mutual regulated and control network, than being given birth to crop
The method more system of model is managed, Consideration is more fully；
3) virtual Breeding Model is built by limited field test, can be in arbitrary growth time with arbitrary breeding
Strategy carries out virtual breeding to crop, the breeding result simulated, different breeding modes, breeding standard is compared,
So as to carry out auxiliary support to breeding practice；
4) performance of breeding progeny can be predicted, saves substantial amounts of time, man power and material's cost.
Brief description of the drawings
The virtual breeding module map of Fig. 1 crops
Virtual breeding procedures figures of the Fig. 2 by taking DH colonies as an example
Embodiment
Present invention structure crop function and structural model, and the simulation of combined amount hereditary information and reproductive process, can
Realize the virtual breeding of crop.With reference to accompanying drawing, the embodiment of this method is as follows：
The virtual breeding method of a kind of crop based on function and structural model, with including physiological function, morphological development, number
Measure the method simulation crop of hereditary information and the crop virtual growth model of specific environmental agents and manual selecting operation's combination
The breeding process of breeding, and comprise the steps of：
Step 1：Data acquisition
By way of the combination of field test, molecular test, assignment of genes gene mapping analysis and literature search, collection model structure institute
The data needed, establish raw data set, wherein including each side data of same target crop colony：Crop growth environment number
According to, form dynamic growth data, major physiological process data, the molecular labeling number related to genetic map data, objective trait
Measure gene data etc.；
Step 2：Function is built with structural model
First, with rule-based Plants modeling method, from computer graphics techniques, with plant growth principle
Based on, using extension XL (eXtended L-Systems) modeling language, Java programming languages and realize figure replace
RGG (Relational Growth Grammar) syntax rule, crop organ's form is rebuild, growth course is entered
Row simulation, establish Crop Structure model (by taking rice as an example, i.e., the visualization built up comprising organ morphologies such as stem, leaf, fringe, seeds
Model)；
Secondly, on the basis of structural model, the simulation of crop major physiological process is added：With LEAFC3 photosynthesis
Model, the photosynthetic parameters related by setting kind, such as maximum carboxylation speed V at 25 DEG Cm25(μmol m-2s-1)、25℃
Potential photosynthetic electron transfer speed J during lower smooth saturationm25(μmol m-2s-1), Photosynthetic Electron transmission activation evergy Ej(J/
Mol), CO at 25 DEG C2Kinetic parameter Kc25(mol/mol), O at 25 DEG C2Kinetic parameter Ko25(mol/mol) etc., and
The unrelated ambient parameter of kind, such as air themperature TaCO in (DEG C), air2Concentration Ca(mol/mol), relative humidity RH, leaf
The two-way long shortwave radiation intensity R that piece absorbsi(W/m2), the wind speed Wspeed (m/s) of horizontal direction etc., simulate the light in blade
Related biochemical reaction, stomatal conductance, the matter and energy transmission mechanism on blade border are closed, with Nikilov et al. 1995
The algorithm delivered in year in the literature calculates the CO of plant leaf blade2Short-term steady state flux, moisture and heat exchange, so as to simulate crop
The speed of assimilation quotient is produced by leaf photosynthesis, is shown below：
An=f (Ri,Ta,...,Vm25,Jm25,...)
Here A is rememberednFor Net Photosynthetic Rate (μm ol m-2s-1), then the photosynthetic yield that each growth step-length is accumulated is：
Here, P is rememberedtFor photosynthetic yield (μm ol), aiFor the area (m of i-th blade2), n is crop plant individual blade
Number, Δ tdFor a growth step-length (s), i.e., the time span of one day in model, it is worth for 24 × 3600, in addition, it is assumed that institute
Some assimilation quotients are all pooled in an assimilation quotient pond being assigned to before organ, are designated as AP (μm ol), then t AP increment
As：
Δ AP=Pt-Gt
Here, G is rememberedtIt is for the amount of the assimilation quotient of plant respiration (including growth respiration and maintenance breathe) consumption, i.e., same
The instantaneous delta Δ AP in compound pond is time t photosynthetic yield PtWith the difference of consumption；GtCumulative by the increment of all organs
Come；The increment of certain organs is calculated by the organ growth function combination source storehouse model：Itd is proposed based on Yin etc. in 2003
Beta growth functions, can be calculated by following formula：
Remember cmFor maximum growth rate, tmFor growth rate maximum at the time of, te(i.e. growth speed at the time of to stop growing
When rate is 0), that is, the dimension of the organ reaches maximum dimension wmax(length m, area m2) at the time of (for example stem reaches
Maximum length or blade reach maximum area), and then the potential growth speed of t certain organs at any time can be calculated
grpot：
By taking the plant height of rice as an example, gr herepotThe as potential growth speed of t plant height, by the potential of all organs
Growth rate adds up, and is multiplied by time step Δ td, obtain the storehouse intensity sd of whole planttot：
sdtot=∑ sspotΔtd
Therefore, the ratio of the storehouse intensity size of individual plants can be accounted for according to the storehouse intensity of certain organs, this is calculated
Actual growth rate gr in organ growth step-lengthreal：
Here AP is the assimilation quotient pond size at current time；Pass through the calculating of these growth functions and partition function, energy
Enough realize that whole plant shows in the developmental stage of whole growth cycle；
Then, emphasis establishes illumination model in environmental model, the position of simulated solar light source and radiancy change；Virtual
Sunshine on high in be divided into direct light and scattering light, including they distribution in three dimensions, and reach crop
Blocking by canopy space after canopy, so as to realize the size of luminous flux in leaf photosynthesis；
Finally, language function-structural modeling technology, with reference to Crop Structure model, physiological models and environmental model, with the time
For axis, regularization explanation is done by formation of the syntax rule based on XL language and RGG to crop organ and growth, and realize
Rule and figure between iteration, replace, with reference to virtual Crop assimilation quotient formed and distribution, so as to combine physiological function,
Realize that virtual Crop Growth trends visualize on the basis of topological structure and luminous environment condition, acquisition can simulate plant growth
The function and structural model of journey；
Step 3：The structure of hereditary module
Increasing hereditary module for individual in model, the module includes the inherent quantitative inheritance attribute stored with array form,
It is designated as：
M={ m1,m2,…,mx,q1,mx+1,…,qi,…,mn}
D={ d12,d23,…,dxq1,…,d(n-1)n}
Here, M represents the molecular marker gene type in one of genome, and its sequence includes n molecular labeling, m1
To mn, the i quantitative trait locuses for studying to obtain by the assignment of genes gene mapping, q placed according to specific location information therebetween1To qi；
The each site value of sequence is 1 or -1, wherein 1 expression molecular labeling m or quantitative trait locus q comes from male parent, -1 table
Show that all molecular marker gene types are all 1 that is, in male parent from female parent, be all -1 in maternal；D represents corresponding molecular labeling
The genetic distance in (or between molecular labeling and quantity site), d between adjacent molecule marks in sequence(n-1)nRepresent the (n-1)th molecule
Genetic distance (Morgan) between mark and n-th of molecular labeling, separately there is an array IqStored number trait locuses institute
The positional information at place, i.e., the subscript in M：
Iq={ I1,I2,…,Ii}
Here, from I1To IiValue represent the 1st quantitative trait locus to the subscript of i-th of locus；
By the computing of above-mentioned two array, the genotype array of quantitative trait locus can be obtained：
Q={ x1,x2,…,xi}
Here array Q represents the genotype of quantitative gene in a genome, xiQ in as above-mentioned M arraysi, simultaneously
The effect value (being represented with array A) in quantitative gene site：
A={ a1,a2,…,ai}
aiRepresent the additive effect value of the quantitative trait locus on i-th of site；According further to actual conditions, aa is established
Epistatic analysis array (AA expressions) and epistatic gene type array (QaaRepresent), i.e., the additive effect and other one in one site
The reciprocal effects and its related locus genotype of the additive effect in individual site：
AA={ aa1,aa2,…,aaj}
Qaa={ xaa1,xaa2,…,xaaj}
aajRepresent j-th plus add epistasis effect value, xaajRepresent the genotype in j-th of epistatic analysis site；According to
These genotype informations and effect value information, along with the community average μ of objective trait, specific strain individual can be calculated
Objective trait Phenotypic value y：
y(Lk)=μ+G (Lk)
Here y (Lk) the individual objective trait Phenotypic value of k-th of strain is represented, by colony's mean μ and strain individual
Genetic effect value G (Lk) plus and and obtain；And genetic effect value is all sites additive effect aiWith loci gene type xi(Lk)Multiply
Long-pending is cumulative, along with all epistasis loci gene type xi(Lk)xj(Lk)With epistatic analysis value aaijProduct；
Here the objective trait Phenotypic value y (L being calculatedk) parameter as genetic correlation, it is updated in step 2
In growth function, instead of wmaxValue, i.e., the maximum growth dimension w in same character growth functionmaxFor genetic correlation, value by
Its genes of individuals offset sum amount site effect value determines；
By taking the plant height character of rice (diploid) as an example, with reference to the genotype data in two genomes and additivity and on
Position property effect Value Data, the Phenotypic value of plant height can be obtained；It is used in trunk diameter growth function and is used as maximum length wmax's
Value, so as to control assimilation quotient competitiveness and growth rate of the individual stem at each growth moment, and realize genotypic difference
To the association of phenotype difference；
Step 4：Simulate reproductive process
First, establish molecular labeling and intersect (crossing over) algorithm：
Step 1：If there is the molecular labeling not traveled through, using the molecular labeling not traveled through as entrance, calculate current adjacent
Exchange rate between molecular labeling (including adjacent molecular labeling and quantitative gene), is calculated by following formula：
Here r is exchange rate, and x is the distance between corresponding molecular labeling value in genetic distance array D in step 3；If
Whole molecular labelings all have stepped through, then perform Step 4；
Step 2：Using the exchange rate calculated as Probability Condition, exchange algorithm is performed, if into exchange algorithm sentence,
Perform Step 3；If being introduced into exchange algorithm sentence, Step 1 is performed；
Step 3：All molecular labelings after current molecular mark swap in two genomes；Perform
Step 1；
Step 4：Execution terminates, and obtains the target molecule marker genetype of corresponding two group chromosome groups；
Then, mainly with chromosome separation and restructuring (Recombination) algorithm simulation reproductive process：
Step 1：(by taking diploid as an example, two genomes for remembering male parent are M to the chromosome of Parent individual11And M12；
Maternal is designated as M21And M22), molecular labeling crossover algorithm is performed respectively, carries out Genome separation, the list after being exchanged
Times genome M’ 11、M’ 12、M’ 21、M’ 22；
Step 2：The two group chromosome group M that male parent isolates are taken with 50% probability’ 11、M’ 12In one group, then equally with
50% probability takes the maternal two group chromosome group M isolated’ 21、M’ 22In one group, be reassembled as filial generation individual chromosome
The composition of group；
Step 3：By the child chromosome group genotype of generation, objective trait is calculated with the method in step 3
The value of parameter, and the growth function applied；
Step 4：Required offspring individual number requirement is such as not up to, then re-executes Step 1；As reached filial generation
The requirement of body number, then this breeding terminate, and may be grown dynamic simulation or breeding next time；
Step 5：Carry out virtual selection and use
By taking seed selection DH colonies as an example, can by with crop function and structural model carry out virtual breeding be divided into it is following several
Step：
Step 1：Initialization model colony, including the related quantitative inheritance information of objective trait is set；
Step 2：Any stage of population growth visual Simulation in a model, select parent of two individuals as hybridization
This；
Step 3：To the genetic manipulation described in parent's execution step 4 of selection, reproductive process is simulated；
Step 4：Obtain son 1 generation colony：F1 generation, perform growth simulation；
Step 5：To continue seed selection, Step 2 is performed；If seed selection finishes, Step 6 is performed；
Step 6：Colony's Genome separation, monoploid is obtained, and carry out simulation and double；
Step 7：Obtain target group：DH colonies, perform growth simulation；
Step 8：The output such as the morphological data of target group, physiological data, genetic data can be obtained by model, it is empty
Intend Breeding Process to terminate；
In addition to the virtual seed selection of DH colonies, above-mentioned steps slightly change the seed selection that can realize other colonies, such as
RIL RIL colonies etc., overall process are that growth simulation → selection → breeding → growth simulation of filial generation → continues to select
→ continue breeding → new filial generation ... and so circulate, selected according to specific standard and target, with regard to that can be simulated
Progeny population, so as to realize the virtual breeding of general significance.
Claims (6)
1. a kind of virtual breeding method of crop based on function and structural model, with including physiological function, morphological development, quantity
The method that hereditary information and the crop virtual growth model of specific environmental agents and manual selecting operation combine simulates the numerous of crop
The breeding process grown, and comprise the steps of：
Step 1：Data acquisition
By way of the combination of field test, molecular test, assignment of genes gene mapping analysis and literature search, needed for collection model structure
Data, raw data set is established, wherein including each side data of same target crop colony：Crop growth environment data, shape
State dynamic growth data, physiological processes data, molecular labeling the quantitative gene number related to genetic map data, objective trait
According to；
Step 2：Function is built with structural model
21. first, with rule-based Plants modeling method, from computer graphics techniques, with plant growth principle
Based on, using extension XL (eXtended L-Systems) modeling language, Java programming languages and realize figure replace
RGG (Relational Growth Grammar) syntax rule, crop organ's form is rebuild, growth course is entered
Row simulation, establishes Crop Structure model；
22. secondly, on the basis of structural model, the simulation of plant physiology process is added：With LEAFC3 Photosynthesis Models,
The photosynthetic parameters related by setting kind, the maximum carboxylation speed V under specified tempm25, light saturation when it is potential photosynthetic
Electron transport rate Jm25, Photosynthetic Electron transmission activation evergy Ej、CO2Kinetic parameter Kc25、O2Kinetic parameter Ko25,
And the ambient parameter that kind is unrelated, including air themperature Ta, CO in air2Concentration Ca, relative humidity RH, blade absorb
Two-way long shortwave radiation intensity Ri, horizontal direction wind speed Wspeed, Vm25Unit be a μm ol m-2s-1, Jm25Unit be μ
mol m-2s-1, activation evergy EjUnit be J/mol, CO2Kinetic parameter Kc25And O2Kinetic parameter Ko25Unit
It is mol/mol, air themperature TaUnit be DEG C CO2Concentration CaUnit be mol/mol, two-way long shortwave radiation intensity Ri
Unit be W/m2, wind speed Wspeed unit is m/s, simulates photosynthetic related biochemical reaction, stomatal conductance, leaf in blade
The matter and energy transmission mechanism of sheet border, calculate the CO of plant leaf blade2Short-term steady state flux, moisture and heat exchange, so as to
The speed that crop produces assimilation quotient by leaf photosynthesis is simulated, is shown below：
An=f (Ri,Ta,...,Vm25,Jm25,...)
Here A is rememberednFor Net Photosynthetic Rate, the unit of Net Photosynthetic Rate is a μm ol m-2s-1, then the light that step-length is accumulated each is grown
Closing yield is：
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m2, n be crop plant individual blade number, Δ tdFor the time span of growth a step-length s, i.e., one day in model, it is worth for 24
× 3600, in addition, it is assumed that all assimilation quotients are all pooled in an assimilation quotient pond being assigned to before organ, it is designated as AP, AP
Unit be a μm ol, then t AP increment is：
Δ AP=Pt-Gt
Here, G is rememberedtFor the amount of the assimilation quotient of plant respiration consumption, i.e. the instantaneous delta Δ AP in assimilation quotient pond is time t
Photosynthetic yield PtWith the difference of consumption, plant respiration includes growth respiration and maintains to breathe；GtBy the increment of all organs
It is cumulative to get；The increment of certain organs is calculated by the organ growth function combination source storehouse model, is calculated by following formula：
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Remember cmFor maximum growth rate, tmFor growth rate maximum at the time of, teAt the time of to stop growing, i.e., growth rate is 0
When, that is, the dimension of the organ reaches maximum growth dimension wmaxAt the time of, i.e., stem reaches maximum length or blade reaches most
At the time of large area, maximum growth dimension wmaxLength be m, area m2, and then calculate the certain organs of t at any time
Potential growth speed grpot：
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grpotThe as potential growth speed of t Plant Height of Rice, by the potential growth speed SS of all organspotIt is cumulative, and multiply
With time step Δ td, obtain the storehouse intensity sd of whole planttot：
sdTot=∑SSpotΔtd
Therefore, the ratio of the storehouse intensity size of individual plants is accounted for according to the storehouse intensity of certain organs, the organ growth is calculated
Actual growth rate gr in step-lengthreal：
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Here AP is the assimilation quotient pond size at current time；Pass through the calculating of these growth functions and partition function, Neng Goushi
Now whole plant shows in the developmental stage of whole growth cycle；
Then, emphasis establishes illumination model in environmental model, the position of simulated solar light source and radiancy change；The virtual sun
Light on high in be divided into direct light and scattering light, including they distribution in three dimensions, and reach crop canopies
Blocking by canopy space afterwards, so as to realize the size of luminous flux in leaf photosynthesis；
23. last, language function, structural modeling technology, with reference to Crop Structure model, physiological models and environmental model, with the time
For axis, regularization explanation is done by formation of the syntax rule based on XL language and RGG to crop organ and growth, and realize
Rule and figure between iteration, replace, with reference to virtual Crop assimilation quotient formed and distribution, so as to combine physiological function,
Realize that virtual Crop Growth trends visualize on the basis of topological structure and luminous environment condition, acquisition can simulate plant growth
The function and structural model of journey；
Step 3：The structure of hereditary module
Increase hereditary module for individual in model, the module is included the inherent quantitative inheritance attribute stored with array form, is designated as：
M={ m1,m2,…,mx,q1,mx+1,…,qi,…,mn}
D={ d12,d23,…,dxqi,…,d(n-1)n}
Here, M represents the molecular marker gene type in one of genome, and its sequence includes n molecular labeling, m1To mn,
The i quantitative trait locuses for studying to obtain by the assignment of genes gene mapping, q placed according to specific location information therebetween1To qi；Sequence
Each site value is 1 or -1, wherein 1 expression molecular labeling m or quantitative trait locus q comes from male parent, -1 represents to come
From female parent, i.e., all molecular marker gene types are all 1 in male parent, are all -1 in maternal；D represents corresponding molecule labelled series
Between middle adjacent molecule mark or the genetic distance between molecular labeling and quantity site, d(n-1)nRepresent the (n-1)th molecular labeling with
Genetic distance between n-th of molecular labeling, dxqiRepresent between x-th of molecular labeling and i-th of quantity site heredity away from
From the unit of genetic distance is Morgan, separately there is an array IqThe location of stored number trait locuses information, that is, exist
Subscript in M：
Iq={ I1,I2,…,Ii}
Here, from I1To IiValue represent the 1st quantitative trait locus to the subscript of i-th of locus；
Pass through above-mentioned M, IqThe computing of two arrays, the genotype array of quantitative trait locus can be obtained：
Q={ x1,x2,…,xi}
Here array Q represents the genotype of quantitative gene in a genome, xiQ in as above-mentioned M arraysi, while quantity
The effect value of gene loci is represented with array A：
A={ a1,a2,…,ai}
aiRepresent the additive effect value of the quantitative trait locus on i-th of site；According further to actual conditions, it is upper to establish aa
Property effect array AA and epistatic gene type array Qaa, i.e., the additive effect in one site and the additive effect in another site
Reciprocal effects and its related locus genotype：
AA={ aa1,aa2,…,aaj}
Qaa={ xaa1,xaa2,…,xaaj}
aajRepresent j-th plus add epistasis effect value, xaajRepresent the genotype in j-th of epistatic analysis site；According to these bases
Because of type information and effect value information, along with the community average μ of objective trait, the individual target of specific strain can be calculated
Trait expression offset y：
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y(Lk)=μ+G (Lk)
Here y (Lk) the individual objective trait Phenotypic value of k-th of strain is represented, by community average μ and the strain individual inheritance
Effect value G (Lk) plus and and obtain；And genetic effect value is the additive effect value a of the quantitative trait locus on i-th of siteiWith
Loci gene type xi(Lk)Product adds up, along with epistasis loci gene type xi(Lk)xj(Lk)With epistatic analysis value aaij's
Product adds up；
Here the objective trait Phenotypic value y (L being calculatedk) parameter as genetic correlation, the growth being updated in step 2
In function, instead of wmaxValue, i.e., the maximum growth dimension w in same character growth functionmaxFor genetic correlation, value is by it
Body genotype value and quantity site effect value determine；
The genotype data and additivity and epistatic analysis in two genomes are combined in the plant height character of diploid rice
Value Data, the Phenotypic value of plant height can be obtained；It is used in trunk diameter growth function and is used as maximum growth dimension wmaxValue,
So as to control assimilation quotient competitiveness and growth rate of the individual stem at each growth moment, and realize genotypic difference to table
The association of existing type difference；
Step 4：Simulate reproductive process
41. first, establishing molecular labeling intersects crossing over algorithms：
Step 411：If there is the molecular labeling not traveled through, using the molecular labeling not traveled through as entrance, calculate current adjacent point
Exchange rate between son mark, including the exchange rate between adjacent molecular labeling and quantitative gene, are calculated by following formula：
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Here r is exchange rate, and x is the distance between corresponding molecular labeling value in genetic distance array D in step 3；If all
Molecular labeling all has stepped through, then performs step 4；
Step 412：Using the exchange rate calculated as Probability Condition, exchange algorithm is performed, if into exchange algorithm sentence, is performed
Step 3；If being introduced into exchange algorithm sentence, step 1 is performed；
Step 413：All molecular labelings after current molecular mark swap in two genomes；Perform step 1；
Step 414：Execution terminates, and obtains the target molecule marker genetype of corresponding two group chromosome groups；
42. then, mainly with chromosome separation and restructuring (Recombination) algorithm simulation reproductive process：
Step 421：The chromosome of Parent individual, two genomes of note diploid male parent are M11And M12, diploid female parent
Be designated as M21And M22, molecular labeling crossover algorithm is performed respectively, carries out Genome separation, and single times after being exchanged contaminates
Colour solid group M '11、M’12、M’21、M’22；
Step 422：The two group chromosome group M ' that male parent isolates are taken with 50% probability11、M’12In one group, then equally with
50% probability takes the maternal two group chromosome group M ' isolated21、M’22In one group, be reassembled as filial generation individual chromosome
The composition of group；
Step 423：By the child chromosome group genotype of generation, objective trait is calculated with the method in step 3
Value, and be applied to as parameter in growth function；
Step 424：Required offspring individual number requirement is such as not up to, then re-executes step 1；As reached offspring individual number
Mesh requirement, then this breeding terminates, and carries out the simulation or breeding next time of Growth trends；
Step 5：Carry out virtual selection and use
In seed selection DH population processeses, it is divided into following several steps by virtual breeding is carried out with crop function and structural model：
Step 51：Initialization model colony, including the related quantitative inheritance information of objective trait is set；
Step 52：Any stage of population growth visual Simulation in a model, select parent of two individuals as hybridization；
Step 53：The molecular labeling performed to the parent of selection described in step 4 intersects, chromosome separation and restructuring, simulation are bred
Process；
Step 54：Obtain son 1 generation colony：F1 generation, perform growth simulation；
Step 55：To continue seed selection, step 52 is performed；If seed selection finishes, step 56 is performed；
Step 56：Colony's Genome separation, monoploid is obtained, and carry out simulation and double；
Step 57：Obtain target group：DH colonies, perform growth simulation；
Step 58：The morphological data of target group, physiological data, genetic data are obtained by model to export, virtual Breeding Process
Terminate；
In addition to the virtual seed selection of DH colonies, above-mentioned steps slightly change the seed selection that can realize other colonies, overall process
It is the filial generation that growth simulation → selection → breeding → growth simulation of filial generation → continues to select → continue breeding → new, so follows
Ring, selected according to specific standard and target, with regard to the progeny population that can be simulated, so as to realize virtual breeding.
2. the virtual breeding method of crop as claimed in claim 1 based on function and structural model, it is characterised in that：The step
Raw data set in rapid 1, growing environment, morphological development data, physiological processes data, number comprising same target crop colony
Hereditary information data are measured, wherein quantitative inheritance information data includes genetic map data, molecular marker gene type data and target
The related quantitative trait locus position of character and effect Value Data, it is interrelated between different types of data, mutually have an impact.
3. the virtual breeding method of crop as claimed in claim 1 based on function and structural model, it is characterised in that：The step
In rapid 2 by LEAFC3 Photosynthesis Models, beta growth functions, assimilation quotient pond, potential growth speed be applied to crop function with
In structural model, structure plant physiology, form, the mutual feedback network of environment, and source and storehouse are associated by assimilation quotient pond, using latent
Calculate the competitiveness of overall storehouse intensity and certain organs to assimilation quotient in growth rate, using assimilation quotient pond and storehouse intensity size come
Photosynthate is distributed, using the final dimension size of organ as key parameter.
4. the virtual breeding method of crop as claimed in claim 1 based on function and structural model, it is characterised in that：The step
In rapid 3, the virtual molecule labelled series of crop individual and other related hereditary information are stored with the method for built-in properties, are passed through
Genetic model and virtual quantitative gene type calculate Phenotypic value, and life is updated to using Phenotypic value as the final dimension of the organ
Potential growth speed is calculated in long function, assimilation quotient is competed, realizes the regulated and control network between gene, physiology, form, environment.
5. the virtual breeding method of crop as claimed in claim 1 based on function and structural model, it is characterised in that：The step
In rapid 4, intersection, the reorganization operation when allele separates are used as by model molecule flag sequence in a model, realized to making
Thing simulates the simulation of individual outbreeding process, wherein by the genetic distance information between molecular labeling calculate adjacent marker it
Between exchange rate, simulation separation after genome, then by simulate Chromosome recombination build offspring individual virtual chromosome,
That is molecule labelled series.
6. the virtual breeding method of crop as claimed in claim 1 based on function and structural model, it is characterised in that：The step
In rapid 5, in any period of plant growth by way of model manipulation, virtual Crop individual is selected, hybridized, be raw
Length, reselection, it is hybridized, and so on, according to specific selection strategy and selection standard, carries out virtual breeding.
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